# _*_coding:utf-8_*_
__author__ = 'gerry'

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

class Perceptron(object):
    """
        Machine_learning classifier.

        Parameters
        -------------
        eta:float
            Learning rate (between 0.0 and 1.0)
        n_iter:int
            Passes over the training dataset.

        Attributes
        -------------
        w_:ld-array
            Weights after fitting
        errors_:list
            Number of misclassifications in every epoch.
    """

    def __init__(self, eta=0.01, n_iter=10):
        self.eta = eta
        self.n_iter = n_iter

    def fit(self, X, y):
        """
            Fit training data

            Parameters
            ---------
            x:{array-like},shape = [n_samples,n_features]
                Training vectors,where
                n_samples is the number of samples and
                n_features is the number of features.
            y:{array-like},shape = [n_samples]
                target values.

            Returns
             self: object


        """

        self.w_ = np.zeros(1 + X.shape[1])
        '''X.shape[[0],[1]]读取矩阵的长度，0：第一维的长度（行），1表示第二维的长度（列）'''
        self.errors_ = []
        for _ in range(self.n_iter):
            errors = 0
            for xi, target in zip(X, y):
                '''zip:返回多个向量组成的列表，并进行转置'''
                update = self.eta * (target - self.predict(xi))
                self.w_[1:] += update * xi
                self.w_[0] += update
                errors += int(update != 0.0)
            self.errors_.append(errors)
        return self

    def net_input(self, X):
        """Calculate net input"""
        return np.dot(X, self.w_[1:]) + self.w_[0]

    def predict(self, X):
        """Return class label after unit step"""
        return np.where(self.net_input(X) >= 0.0, 1, -1)

